{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "10b7328c-03a0-4ab5-a451-eee98801bd55",
   "metadata": {},
   "source": [
    "# Composite Adversarial Attacks in PyTorch\n",
    "This notebook provides a demonstration showing how to use ART to launch the composite adversarial attack (CAA) [1]. CAA consists of the following perturbations:\n",
    "\n",
    "- Hue\n",
    "- Saturation\n",
    "- Rotation\n",
    "- Brightness\n",
    "- Contrast\n",
    "- PGD ($\\ell_\\infty$)\n",
    "\n",
    "[1] Towards Compositional Adversarial Robustness: Generalizing Adversarial Training to Composite Semantic Perturbations (CVPR 2023)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "53cf6ef7-9572-4a30-a71e-8315844a5c3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from art.attacks.evasion import CompositeAdversarialAttackPyTorch\n",
    "from art.estimators.classification import PyTorchClassifier\n",
    "from art.utils import load_cifar10\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2f689043-e7c8-4e87-b49c-5ff7d11a447f",
   "metadata": {},
   "outputs": [],
   "source": [
    "(x_train, y_train), (x_test, y_test), min_pixel_value, max_pixel_value = load_cifar10()\n",
    "\n",
    "# Swap the axes to PyTorch's NCHW format.\n",
    "x_train = np.transpose(x_train, (0, 3, 1, 2)).astype(np.float32)\n",
    "x_test = np.transpose(x_test, (0, 3, 1, 2)).astype(np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e5775160-2a0b-49bc-9bf6-57a9613906e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a simple convolutional neural network.\n",
    "model = nn.Sequential(\n",
    "    nn.Conv2d(3, 8, 5), nn.BatchNorm2d(8), nn.ReLU(), nn.MaxPool2d(2, 2), \n",
    "    nn.Conv2d(8, 16, 5), nn.BatchNorm2d(16), nn.ReLU(), nn.MaxPool2d(2, 2),\n",
    "    nn.Flatten(), \n",
    "    nn.Linear(5*5*16, 128),    \n",
    "    nn.Linear(128, 10)\n",
    ")\n",
    "\n",
    "# Define the loss function and the optimizer.\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=0.01)\n",
    "\n",
    "# Create the ART classifier.\n",
    "classifier = PyTorchClassifier(\n",
    "    model=model,\n",
    "    clip_values=(min_pixel_value, max_pixel_value),\n",
    "    loss=criterion,\n",
    "    optimizer=optimizer,\n",
    "    input_shape=(3, 32, 32),\n",
    "    nb_classes=10,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7cdda5a3-d7a3-4210-a47c-fd5571e3ea92",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on the benign test set: 60.440000000000005%\n"
     ]
    }
   ],
   "source": [
    "# Train the ART classifier.\n",
    "classifier.fit(x_train, y_train, batch_size=64, nb_epochs=5, verbose=True)\n",
    "\n",
    "# Evaluate the ART classifier on benign test examples.\n",
    "predictions_benign = classifier.predict(x_test)\n",
    "\n",
    "accuracy = np.sum(np.argmax(predictions_benign, axis=1) == np.argmax(y_test, axis=1)) / len(y_test)\n",
    "print(\"Accuracy on the benign test set: {}%\".format(accuracy * 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b45e556-d405-4620-8a0f-77628b8c5e33",
   "metadata": {},
   "source": [
    "## Launch Composite Adversarial Attack\n",
    "\n",
    "`CompositeAdversarialAttack` has the following parameters:\n",
    "- `classifier`: A trained PyTorch classifier.\n",
    "- `enabled_attack`: Attack pool selection, and attack order designation for `fixed` order. For simplicity, we use the following abbreviations to specify each attack type. 0: Hue, 1: Saturation, 2: Rotation, 3: Brightness, 4: Contrast, 5: PGD ($\\ell_\\infty$).\n",
    "- `hue_epsilon`: The boundary of the hue perturbation. The value is expected to be in the interval `[-np.pi, np.pi]`. Perturbation of `0` means no shift and `-np.pi` and `np.pi` give a complete reversal of the hue channel in the HSV color space in the positive and negative directions, respectively. See `kornia.enhance.adjust_hue` for more details.\n",
    "- `sat_epsilon`: The boundary of the saturation perturbation. The value is expected to be in the interval `[0.0, infinity]`. The perturbation of `0.0` gives a black-and-white image, `1.0` gives the original image, and `2.0` enhances the saturation by a factor of 2. See `kornia.geometry.transform.rotate` for more details.\n",
    "- `rot_epsilon`: The boundary of the rotation perturbation (in degrees). Positive values mean counter-clockwise rotation. See `kornia.geometry.transform.rotate` for more details.\n",
    "- `bri_epsilon`: The boundary of the brightness perturbation. The value is expected to be in the interval `[-1.0, 1.0]`. Perturbation of `0.0` means no shift, `-1.0` gives a complete black image, and `1.0` gives a complete white image. See `kornia.enhance.adjust_brightness` for more details.\n",
    "- `con_epsilon`: The boundary of the contrast perturbation. The value is expected to be in the interval `[0.0, infinity]`. Perturbation of `0.0` gives a complete black image, `1` does not modify the image, and any other value modifies the brightness by this factor. See `kornia.enhance.adjust_contrast` for more details.\n",
    "- `pgd_epsilon`: The maximum perturbation that the attacker can introduce in the L-infinity ball.\n",
    "- `early_stop`: When True, the attack will stop if the perturbed example is classified incorrectly by the classifier.\n",
    "- `max_iter`: The maximum number of iterations for attack order optimization.\n",
    "- `max_inner_iter`: The maximum number of iterations for each attack optimization.\n",
    "- `attack_order`: Specify the scheduling type for the composite adversarial attack. The value is expected to be `fixed`, `random`, or `scheduled`. `fixed` means the attack order is the same as specified in `enabled_attack`. `random` means the attack order is randomly generated at each iteration. `scheduled` means to enable the attack order optimization proposed in the paper. If only one attack is enabled, `fixed` will be used.\n",
    "- `batch_size`: The batch size to use during the generation of adversarial samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f36c10c0-ddb6-4787-8b97-41e1f228ea62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Composite Adversarial Attack:   0%|          | 0/40 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on the adversarial test set: 0.0%\n"
     ]
    }
   ],
   "source": [
    "# Create the ART attacker.\n",
    "attack = CompositeAdversarialAttackPyTorch(classifier,\n",
    "         enabled_attack = (0,1,2,3,4,5), # 0: Hue, 1: Saturation, 2: Rotation, 3: Brightness, 4: Contrast, 5: PGD (L-infinity)\n",
    "         hue_epsilon = (-np.pi, np.pi),\n",
    "         sat_epsilon = (0.7, 1.3),\n",
    "         rot_epsilon = (-10, 10),\n",
    "         bri_epsilon = (-0.2, 0.2),\n",
    "         con_epsilon = (0.7, 1.3),\n",
    "         pgd_epsilon = (-8 / 255, 8 / 255),\n",
    "         early_stop = True,\n",
    "         max_iter = 5,\n",
    "         max_inner_iter = 10,\n",
    "         attack_order = \"scheduled\", # \"scheduled\", \"random\", or \"fixed\"\n",
    "         batch_size = 1,\n",
    ")\n",
    "\n",
    "# Generate the adversarial examples.\n",
    "x_test_adv = attack.generate(x_test, y_test)\n",
    "\n",
    "\n",
    "# Evaluate the ART classifier on the adversarial examples.\n",
    "predictions_adv = classifier.predict(x_test_adv)\n",
    "accuracy = np.sum(np.argmax(predictions_adv, axis=1) == np.argmax(y_test, axis=1)) / len(y_test)\n",
    "print(\"Accuracy on the adversarial test set: {}%\".format(accuracy * 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a76d108d-ee48-4a26-b6fd-3c1945e2e891",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "# Utility function for visualising some results.\n",
    "def visualise(x_test, x_test_adv, predictions_benign, predictions_adv, y_test,\n",
    "              num_viz=3):\n",
    "      # Indices where (predictions_adv != predictions_benign) and (predictions_benign == y_test)\n",
    "      indices = np.where(np.logical_and(\n",
    "            np.equal(np.argmax(predictions_benign, axis=1), np.argmax(y_test, axis=1)),\n",
    "            np.not_equal(np.argmax(predictions_benign, axis=1), np.argmax(predictions_adv, axis=1))))\n",
    "\n",
    "      x_adv_viz = x_test_adv[indices].transpose(0, 2, 3, 1) * 255.0\n",
    "      x_adv_viz = x_adv_viz.astype(np.uint8)\n",
    "      x_viz = x_test[indices].transpose(0, 2, 3, 1) * 255.0\n",
    "      x_viz = x_viz.astype(np.uint8)\n",
    "      y_viz = y_test[indices]\n",
    "      predictions_benign_viz = predictions_benign[indices]\n",
    "      predictions_adv_viz = predictions_adv[indices]\n",
    "\n",
    "      for i in range(num_viz):\n",
    "            fig, ax = plt.subplots(1, 2)\n",
    "            ax[0].imshow(x_adv_viz[i])\n",
    "            ax[0].set_title(\"Prediction after: {}\".format(np.argmax(predictions_adv_viz, axis=1)[i]))\n",
    "            ax[1].imshow(x_viz[i])\n",
    "            ax[1].set_title(\"Prediction before: {}\".format(np.argmax(predictions_benign_viz, axis=1)[i]))\n",
    "            plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b9dee647-5194-48a0-9b0e-a1799c4f8052",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualise(x_test, x_test_adv, predictions_benign, predictions_adv, y_test)"
   ]
  },
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   "execution_count": null,
   "id": "7eab976f-84ab-4a4e-927a-a0761907517b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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